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Supervised Memory Training enables RNNs to learn long-range dependencies more effectively while training in parallel, outperforming traditional methods.
LLMs trained with Vector Policy Optimization (VPO) learn to produce diverse solutions that unlock previously unsolvable problems in evolutionary search, outperforming models optimized for single scalar rewards.
